Topic: reward signal

Dopaminergic reward prediction error neurons in the midbrain are the most prominent reward representation in the brain. To understand why these neurons represent rewards the way they do when stochastic rewards are encountered, we derive the optimal population of neurons to encode rewards drawn from a given distribution. To do so, we extend an existing framework for efficient coding to decouple the density of neurons from their slopes. We compare the optimally efficient population to measurements of reward prediction error neurons. We find three aspects of the optimized population confirmed: Fi